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<h1 id="sec_name">
<span data-if="hdevelop" style="display:inline;">create_class_lut_knn</span><span data-if="c" style="display:none;">T_create_class_lut_knn</span><span data-if="cpp" style="display:none;">CreateClassLutKnn</span><span data-if="dotnet" style="display:none;">CreateClassLutKnn</span><span data-if="python" style="display:none;">create_class_lut_knn</span> (算子名称)</h1>
<h2>名称</h2>
<p><code><span data-if="hdevelop" style="display:inline;">create_class_lut_knn</span><span data-if="c" style="display:none;">T_create_class_lut_knn</span><span data-if="cpp" style="display:none;">CreateClassLutKnn</span><span data-if="dotnet" style="display:none;">CreateClassLutKnn</span><span data-if="python" style="display:none;">create_class_lut_knn</span></code> — Create a look-up table using a K-最近邻
classifier (k-NN) to classify byte
images.</p>
<h2 id="sec_synopsis">参数签名</h2>
<div data-if="hdevelop" style="display:inline;">
<p>
<code><b>create_class_lut_knn</b>( :  : <a href="#KNNHandle"><i>KNNHandle</i></a>, <a href="#GenParamName"><i>GenParamName</i></a>, <a href="#GenParamValue"><i>GenParamValue</i></a> : <a href="#ClassLUTHandle"><i>ClassLUTHandle</i></a>)</code></p>
</div>
<div data-if="c" style="display:none;">
<p>
<code>Herror <b>T_create_class_lut_knn</b>(const Htuple <a href="#KNNHandle"><i>KNNHandle</i></a>, const Htuple <a href="#GenParamName"><i>GenParamName</i></a>, const Htuple <a href="#GenParamValue"><i>GenParamValue</i></a>, Htuple* <a href="#ClassLUTHandle"><i>ClassLUTHandle</i></a>)</code></p>
</div>
<div data-if="cpp" style="display:none;">
<p>
<code>void <b>CreateClassLutKnn</b>(const HTuple&amp; <a href="#KNNHandle"><i>KNNHandle</i></a>, const HTuple&amp; <a href="#GenParamName"><i>GenParamName</i></a>, const HTuple&amp; <a href="#GenParamValue"><i>GenParamValue</i></a>, HTuple* <a href="#ClassLUTHandle"><i>ClassLUTHandle</i></a>)</code></p>
<p>
<code>void <a href="HClassLUT.html">HClassLUT</a>::<b>HClassLUT</b>(const HClassKnn&amp; <a href="#KNNHandle"><i>KNNHandle</i></a>, const HTuple&amp; <a href="#GenParamName"><i>GenParamName</i></a>, const HTuple&amp; <a href="#GenParamValue"><i>GenParamValue</i></a>)</code></p>
<p>
<code>void <a href="HClassLUT.html">HClassLUT</a>::<b>CreateClassLutKnn</b>(const HClassKnn&amp; <a href="#KNNHandle"><i>KNNHandle</i></a>, const HTuple&amp; <a href="#GenParamName"><i>GenParamName</i></a>, const HTuple&amp; <a href="#GenParamValue"><i>GenParamValue</i></a>)</code></p>
<p>
<code><a href="HClassLUT.html">HClassLUT</a> <a href="HClassKnn.html">HClassKnn</a>::<b>CreateClassLutKnn</b>(const HTuple&amp; <a href="#GenParamName"><i>GenParamName</i></a>, const HTuple&amp; <a href="#GenParamValue"><i>GenParamValue</i></a>) const</code></p>
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<p>
<code>static void <a href="HOperatorSet.html">HOperatorSet</a>.<b>CreateClassLutKnn</b>(<a href="HTuple.html">HTuple</a> <a href="#KNNHandle"><i>KNNHandle</i></a>, <a href="HTuple.html">HTuple</a> <a href="#GenParamName"><i>genParamName</i></a>, <a href="HTuple.html">HTuple</a> <a href="#GenParamValue"><i>genParamValue</i></a>, out <a href="HTuple.html">HTuple</a> <a href="#ClassLUTHandle"><i>classLUTHandle</i></a>)</code></p>
<p>
<code>public <a href="HClassLUT.html">HClassLUT</a>(<a href="HClassKnn.html">HClassKnn</a> <a href="#KNNHandle"><i>KNNHandle</i></a>, <a href="HTuple.html">HTuple</a> <a href="#GenParamName"><i>genParamName</i></a>, <a href="HTuple.html">HTuple</a> <a href="#GenParamValue"><i>genParamValue</i></a>)</code></p>
<p>
<code>void <a href="HClassLUT.html">HClassLUT</a>.<b>CreateClassLutKnn</b>(<a href="HClassKnn.html">HClassKnn</a> <a href="#KNNHandle"><i>KNNHandle</i></a>, <a href="HTuple.html">HTuple</a> <a href="#GenParamName"><i>genParamName</i></a>, <a href="HTuple.html">HTuple</a> <a href="#GenParamValue"><i>genParamValue</i></a>)</code></p>
<p>
<code><a href="HClassLUT.html">HClassLUT</a> <a href="HClassKnn.html">HClassKnn</a>.<b>CreateClassLutKnn</b>(<a href="HTuple.html">HTuple</a> <a href="#GenParamName"><i>genParamName</i></a>, <a href="HTuple.html">HTuple</a> <a href="#GenParamValue"><i>genParamValue</i></a>)</code></p>
</div>
<div data-if="python" style="display:none;">
<p>
<code>def <b>create_class_lut_knn</b>(<a href="#KNNHandle"><i>knnhandle</i></a>: HHandle, <a href="#GenParamName"><i>gen_param_name</i></a>: Sequence[str], <a href="#GenParamValue"><i>gen_param_value</i></a>: Sequence[Union[str, int, float]]) -&gt; HHandle</code></p>
</div>
<h2 id="sec_description">描述</h2>
<p><code><span data-if="hdevelop" style="display:inline">create_class_lut_knn</span><span data-if="c" style="display:none">create_class_lut_knn</span><span data-if="cpp" style="display:none">CreateClassLutKnn</span><span data-if="com" style="display:none">CreateClassLutKnn</span><span data-if="dotnet" style="display:none">CreateClassLutKnn</span><span data-if="python" style="display:none">create_class_lut_knn</span></code> generates a look-up table (LUT)
<a href="#ClassLUTHandle"><i><code><span data-if="hdevelop" style="display:inline">ClassLUTHandle</span><span data-if="c" style="display:none">ClassLUTHandle</span><span data-if="cpp" style="display:none">ClassLUTHandle</span><span data-if="com" style="display:none">ClassLUTHandle</span><span data-if="dotnet" style="display:none">classLUTHandle</span><span data-if="python" style="display:none">class_luthandle</span></code></i></a> using the data of a trained K-最近邻
classifier (k-NN) <a href="#KNNHandle"><i><code><span data-if="hdevelop" style="display:inline">KNNHandle</span><span data-if="c" style="display:none">KNNHandle</span><span data-if="cpp" style="display:none">KNNHandle</span><span data-if="com" style="display:none">KNNHandle</span><span data-if="dotnet" style="display:none">KNNHandle</span><span data-if="python" style="display:none">knnhandle</span></code></i></a> to classify multi-channel byte images.
By using this k-NN-based LUT classifier, 该算子
<a href="classify_image_class_knn.html"><code><span data-if="hdevelop" style="display:inline">classify_image_class_knn</span><span data-if="c" style="display:none">classify_image_class_knn</span><span data-if="cpp" style="display:none">ClassifyImageClassKnn</span><span data-if="com" style="display:none">ClassifyImageClassKnn</span><span data-if="dotnet" style="display:none">ClassifyImageClassKnn</span><span data-if="python" style="display:none">classify_image_class_knn</span></code></a> of the subsequent classification can be
replaced by 该算子 <a href="classify_image_class_lut.html"><code><span data-if="hdevelop" style="display:inline">classify_image_class_lut</span><span data-if="c" style="display:none">classify_image_class_lut</span><span data-if="cpp" style="display:none">ClassifyImageClassLut</span><span data-if="com" style="display:none">ClassifyImageClassLut</span><span data-if="dotnet" style="display:none">ClassifyImageClassLut</span><span data-if="python" style="display:none">classify_image_class_lut</span></code></a>. The
classification is speed up considerably, because the estimation
of the class in every image point is no longer
necessary since every possible response of the k-NN is stored in the LUT.
For the generation of the  LUT, the parameter <code><span data-if="hdevelop" style="display:inline">NumDim</span><span data-if="c" style="display:none">NumDim</span><span data-if="cpp" style="display:none">NumDim</span><span data-if="com" style="display:none">NumDim</span><span data-if="dotnet" style="display:none">numDim</span><span data-if="python" style="display:none">num_dim</span></code> of
called operator <a href="create_class_knn.html"><code><span data-if="hdevelop" style="display:inline">create_class_knn</span><span data-if="c" style="display:none">create_class_knn</span><span data-if="cpp" style="display:none">CreateClassKnn</span><span data-if="com" style="display:none">CreateClassKnn</span><span data-if="dotnet" style="display:none">CreateClassKnn</span><span data-if="python" style="display:none">create_class_knn</span></code></a> is important.
The number of image channels the images must have to be
classified is defined in <code><span data-if="hdevelop" style="display:inline">NumDim</span><span data-if="c" style="display:none">NumDim</span><span data-if="cpp" style="display:none">NumDim</span><span data-if="com" style="display:none">NumDim</span><span data-if="dotnet" style="display:none">numDim</span><span data-if="python" style="display:none">num_dim</span></code>.
</p>
<p>To create the LUT,
all pixel values are classified with <a href="classify_class_knn.html"><code><span data-if="hdevelop" style="display:inline">classify_class_knn</span><span data-if="c" style="display:none">classify_class_knn</span><span data-if="cpp" style="display:none">ClassifyClassKnn</span><span data-if="com" style="display:none">ClassifyClassKnn</span><span data-if="dotnet" style="display:none">ClassifyClassKnn</span><span data-if="python" style="display:none">classify_class_knn</span></code></a>.
The returned classes are stored in the LUT. Because of the discretization
of the LUT, the accuracy of the LUT classifier could become lower than the
accuracy of <a href="classify_image_class_knn.html"><code><span data-if="hdevelop" style="display:inline">classify_image_class_knn</span><span data-if="c" style="display:none">classify_image_class_knn</span><span data-if="cpp" style="display:none">ClassifyImageClassKnn</span><span data-if="com" style="display:none">ClassifyImageClassKnn</span><span data-if="dotnet" style="display:none">ClassifyImageClassKnn</span><span data-if="python" style="display:none">classify_image_class_knn</span></code></a>.
</p>
<p>With <i><span data-if="hdevelop" style="display:inline">'bit_depth'</span><span data-if="c" style="display:none">"bit_depth"</span><span data-if="cpp" style="display:none">"bit_depth"</span><span data-if="com" style="display:none">"bit_depth"</span><span data-if="dotnet" style="display:none">"bit_depth"</span><span data-if="python" style="display:none">"bit_depth"</span></i> the accuracy of the classification, the
required storage, and the runtime needed to create
the LUT can be controlled.
</p>
<p>The following parameters of the k-NN-based LUT classifier can be set with
<a href="#GenParamName"><i><code><span data-if="hdevelop" style="display:inline">GenParamName</span><span data-if="c" style="display:none">GenParamName</span><span data-if="cpp" style="display:none">GenParamName</span><span data-if="com" style="display:none">GenParamName</span><span data-if="dotnet" style="display:none">genParamName</span><span data-if="python" style="display:none">gen_param_name</span></code></i></a> and <a href="#GenParamValue"><i><code><span data-if="hdevelop" style="display:inline">GenParamValue</span><span data-if="c" style="display:none">GenParamValue</span><span data-if="cpp" style="display:none">GenParamValue</span><span data-if="com" style="display:none">GenParamValue</span><span data-if="dotnet" style="display:none">genParamValue</span><span data-if="python" style="display:none">gen_param_value</span></code></i></a>:
</p>
<dl class="generic">

<dt><b><i><span data-if="hdevelop" style="display:inline">'bit_depth'</span><span data-if="c" style="display:none">"bit_depth"</span><span data-if="cpp" style="display:none">"bit_depth"</span><span data-if="com" style="display:none">"bit_depth"</span><span data-if="dotnet" style="display:none">"bit_depth"</span><span data-if="python" style="display:none">"bit_depth"</span></i>:</b></dt>
<dd>
<p>

Number of bits used from the pixels. It controls the storage requirement
of the LUT classifier and is bounded by the bit depth of the image
(<i><span data-if="hdevelop" style="display:inline">'bit_depth'</span><span data-if="c" style="display:none">"bit_depth"</span><span data-if="cpp" style="display:none">"bit_depth"</span><span data-if="com" style="display:none">"bit_depth"</span><span data-if="dotnet" style="display:none">"bit_depth"</span><span data-if="python" style="display:none">"bit_depth"</span></i> &lt;= <i>8</i>). If the bit depth of the
LUT is smaller (<i><span data-if="hdevelop" style="display:inline">'bit_depth'</span><span data-if="c" style="display:none">"bit_depth"</span><span data-if="cpp" style="display:none">"bit_depth"</span><span data-if="com" style="display:none">"bit_depth"</span><span data-if="dotnet" style="display:none">"bit_depth"</span><span data-if="python" style="display:none">"bit_depth"</span></i> &lt; <i>8</i>), the classes
of multiple pixel combinations will be mapped to the same LUT entry,
which can result in a lower accuracy for the classification. One of these
clusters contains
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</svg></span> pixel combinations,
where <code><span data-if="hdevelop" style="display:inline">NumDim</span><span data-if="c" style="display:none">NumDim</span><span data-if="cpp" style="display:none">NumDim</span><span data-if="com" style="display:none">NumDim</span><span data-if="dotnet" style="display:none">numDim</span><span data-if="python" style="display:none">num_dim</span></code> denotes the dimension of the LUT, which is
specified in <a href="create_class_knn.html"><code><span data-if="hdevelop" style="display:inline">create_class_knn</span><span data-if="c" style="display:none">create_class_knn</span><span data-if="cpp" style="display:none">CreateClassKnn</span><span data-if="com" style="display:none">CreateClassKnn</span><span data-if="dotnet" style="display:none">CreateClassKnn</span><span data-if="python" style="display:none">create_class_knn</span></code></a>. For example,
for <i><span data-if="hdevelop" style="display:inline">'bit_depth'</span><span data-if="c" style="display:none">"bit_depth"</span><span data-if="cpp" style="display:none">"bit_depth"</span><span data-if="com" style="display:none">"bit_depth"</span><span data-if="dotnet" style="display:none">"bit_depth"</span><span data-if="python" style="display:none">"bit_depth"</span></i> = <i>7</i>,
<code><span data-if="hdevelop" style="display:inline">NumDim</span><span data-if="c" style="display:none">NumDim</span><span data-if="cpp" style="display:none">NumDim</span><span data-if="com" style="display:none">NumDim</span><span data-if="dotnet" style="display:none">numDim</span><span data-if="python" style="display:none">num_dim</span></code> = <i>3</i>, the classes of  <i>8</i> pixel
combinations are mapped in the same LUT entry. The LUT requires at most
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</svg></span> bytes of storage.
For example, for <code><span data-if="hdevelop" style="display:inline">NumDim</span><span data-if="c" style="display:none">NumDim</span><span data-if="cpp" style="display:none">NumDim</span><span data-if="com" style="display:none">NumDim</span><span data-if="dotnet" style="display:none">numDim</span><span data-if="python" style="display:none">num_dim</span></code> = <i>3</i>,
<i><span data-if="hdevelop" style="display:inline">'bit_depth'</span><span data-if="c" style="display:none">"bit_depth"</span><span data-if="cpp" style="display:none">"bit_depth"</span><span data-if="com" style="display:none">"bit_depth"</span><span data-if="dotnet" style="display:none">"bit_depth"</span><span data-if="python" style="display:none">"bit_depth"</span></i> = <i>8</i> and number of classes is smaller than
<i>16</i>, the LUT requires 8 MB of storage
with internal storage optimization.
The runtime for the classification in <a href="classify_image_class_lut.html"><code><span data-if="hdevelop" style="display:inline">classify_image_class_lut</span><span data-if="c" style="display:none">classify_image_class_lut</span><span data-if="cpp" style="display:none">ClassifyImageClassLut</span><span data-if="com" style="display:none">ClassifyImageClassLut</span><span data-if="dotnet" style="display:none">ClassifyImageClassLut</span><span data-if="python" style="display:none">classify_image_class_lut</span></code></a>
becomes minimal if the LUT fits into the cache.</p>
<p>
<i>Typical values:</i> <i>6</i>,<i>7</i>,<i>8</i></p>
<p>
<i>Default:</i> <i>8</i></p>
<p>
<i>Restriction:</i> <i><span data-if="hdevelop" style="display:inline">'bit_depth'</span><span data-if="c" style="display:none">"bit_depth"</span><span data-if="cpp" style="display:none">"bit_depth"</span><span data-if="com" style="display:none">"bit_depth"</span><span data-if="dotnet" style="display:none">"bit_depth"</span><span data-if="python" style="display:none">"bit_depth"</span></i> &gt;= <i>1</i>,
<i><span data-if="hdevelop" style="display:inline">'bit_depth'</span><span data-if="c" style="display:none">"bit_depth"</span><span data-if="cpp" style="display:none">"bit_depth"</span><span data-if="com" style="display:none">"bit_depth"</span><span data-if="dotnet" style="display:none">"bit_depth"</span><span data-if="python" style="display:none">"bit_depth"</span></i> &lt;= <i>8</i>.
</p>
</dd>

<dt><b><i><span data-if="hdevelop" style="display:inline">'rejection_threshold'</span><span data-if="c" style="display:none">"rejection_threshold"</span><span data-if="cpp" style="display:none">"rejection_threshold"</span><span data-if="com" style="display:none">"rejection_threshold"</span><span data-if="dotnet" style="display:none">"rejection_threshold"</span><span data-if="python" style="display:none">"rejection_threshold"</span></i>:</b></dt>
<dd>
<p>

Threshold for the rejection of uncertain classified points of the k-NN.
The parameter represents a threshold on the distance
returned by the classification (see <a href="classify_class_knn.html"><code><span data-if="hdevelop" style="display:inline">classify_class_knn</span><span data-if="c" style="display:none">classify_class_knn</span><span data-if="cpp" style="display:none">ClassifyClassKnn</span><span data-if="com" style="display:none">ClassifyClassKnn</span><span data-if="dotnet" style="display:none">ClassifyClassKnn</span><span data-if="python" style="display:none">classify_class_knn</span></code></a>).
All pixels having a distance over
<i><span data-if="hdevelop" style="display:inline">'rejection_threshold'</span><span data-if="c" style="display:none">"rejection_threshold"</span><span data-if="cpp" style="display:none">"rejection_threshold"</span><span data-if="com" style="display:none">"rejection_threshold"</span><span data-if="dotnet" style="display:none">"rejection_threshold"</span><span data-if="python" style="display:none">"rejection_threshold"</span></i> are not assigned to any class.</p>
<p>
<i>Default:</i> <i>5</i></p>
<p>
<i>Restriction:</i> <i><span data-if="hdevelop" style="display:inline">'rejection_threshold'</span><span data-if="c" style="display:none">"rejection_threshold"</span><span data-if="cpp" style="display:none">"rejection_threshold"</span><span data-if="com" style="display:none">"rejection_threshold"</span><span data-if="dotnet" style="display:none">"rejection_threshold"</span><span data-if="python" style="display:none">"rejection_threshold"</span></i> &gt;= <i>0</i>.
</p>
</dd>
</dl>
<h2 id="sec_execution">运行信息</h2>
<ul>
  <li>多线程类型:可重入(与非独占操作符并行运行)。</li>
<li>多线程作用域:全局(可以从任何线程调用)。</li>
  
    <li>Automatically parallelized on internal data level.</li>
  
</ul>
<p>This operator returns a handle. Note that the state of an instance of this handle type may be changed by specific operators even though the handle is used as an input parameter by those operators.</p>
<h2 id="sec_parameters">参数表</h2>
  <div class="par">
<div class="parhead">
<span id="KNNHandle" class="parname"><b><code><span data-if="hdevelop" style="display:inline">KNNHandle</span><span data-if="c" style="display:none">KNNHandle</span><span data-if="cpp" style="display:none">KNNHandle</span><span data-if="com" style="display:none">KNNHandle</span><span data-if="dotnet" style="display:none">KNNHandle</span><span data-if="python" style="display:none">knnhandle</span></code></b> (input_control)  </span><span>class_knn <code>→</code> <span data-if="dotnet" style="display:none"><a href="HClassKnn.html">HClassKnn</a>, </span><span data-if="dotnet" style="display:none"><a href="HTuple.html">HTuple</a></span><span data-if="python" style="display:none">HHandle</span><span data-if="cpp" style="display:none"><a href="HTuple.html">HTuple</a></span><span data-if="c" style="display:none">Htuple</span><span data-if="hdevelop" style="display:inline"> (handle)</span><span data-if="dotnet" style="display:none"> (<i>IntPtr</i>)</span><span data-if="cpp" style="display:none"> (<i>HHandle</i>)</span><span data-if="c" style="display:none"> (<i>handle</i>)</span></span>
</div>
<p class="pardesc">Handle of the k-NN classifier.</p>
</div>
  <div class="par">
<div class="parhead">
<span id="GenParamName" class="parname"><b><code><span data-if="hdevelop" style="display:inline">GenParamName</span><span data-if="c" style="display:none">GenParamName</span><span data-if="cpp" style="display:none">GenParamName</span><span data-if="com" style="display:none">GenParamName</span><span data-if="dotnet" style="display:none">genParamName</span><span data-if="python" style="display:none">gen_param_name</span></code></b> (input_control)  </span><span>attribute.name-array <code>→</code> <span data-if="dotnet" style="display:none"><a href="HTuple.html">HTuple</a></span><span data-if="python" style="display:none">Sequence[str]</span><span data-if="cpp" style="display:none"><a href="HTuple.html">HTuple</a></span><span data-if="c" style="display:none">Htuple</span><span data-if="hdevelop" style="display:inline"> (string)</span><span data-if="dotnet" style="display:none"> (<i>string</i>)</span><span data-if="cpp" style="display:none"> (<i>HString</i>)</span><span data-if="c" style="display:none"> (<i>char*</i>)</span></span>
</div>
<p class="pardesc">Names of the generic parameters that can be adjusted
for the LUT classifier creation.</p>
<p class="pardesc"><span class="parcat">Default:
      </span>[]</p>
<p class="pardesc"><span class="parcat">Suggested values:
      </span><span data-if="hdevelop" style="display:inline">'bit_depth'</span><span data-if="c" style="display:none">"bit_depth"</span><span data-if="cpp" style="display:none">"bit_depth"</span><span data-if="com" style="display:none">"bit_depth"</span><span data-if="dotnet" style="display:none">"bit_depth"</span><span data-if="python" style="display:none">"bit_depth"</span>, <span data-if="hdevelop" style="display:inline">'rejection_threshold'</span><span data-if="c" style="display:none">"rejection_threshold"</span><span data-if="cpp" style="display:none">"rejection_threshold"</span><span data-if="com" style="display:none">"rejection_threshold"</span><span data-if="dotnet" style="display:none">"rejection_threshold"</span><span data-if="python" style="display:none">"rejection_threshold"</span></p>
</div>
  <div class="par">
<div class="parhead">
<span id="GenParamValue" class="parname"><b><code><span data-if="hdevelop" style="display:inline">GenParamValue</span><span data-if="c" style="display:none">GenParamValue</span><span data-if="cpp" style="display:none">GenParamValue</span><span data-if="com" style="display:none">GenParamValue</span><span data-if="dotnet" style="display:none">genParamValue</span><span data-if="python" style="display:none">gen_param_value</span></code></b> (input_control)  </span><span>attribute.value-array <code>→</code> <span data-if="dotnet" style="display:none"><a href="HTuple.html">HTuple</a></span><span data-if="python" style="display:none">Sequence[Union[str, int, float]]</span><span data-if="cpp" style="display:none"><a href="HTuple.html">HTuple</a></span><span data-if="c" style="display:none">Htuple</span><span data-if="hdevelop" style="display:inline"> (string / </span><span data-if="hdevelop" style="display:inline">integer / </span><span data-if="hdevelop" style="display:inline">real)</span><span data-if="dotnet" style="display:none"> (<i>string</i> / </span><span data-if="dotnet" style="display:none">int / </span><span data-if="dotnet" style="display:none">long / </span><span data-if="dotnet" style="display:none">double)</span><span data-if="cpp" style="display:none"> (<i>HString</i> / </span><span data-if="cpp" style="display:none">Hlong / </span><span data-if="cpp" style="display:none">double)</span><span data-if="c" style="display:none"> (<i>char*</i> / </span><span data-if="c" style="display:none">Hlong / </span><span data-if="c" style="display:none">double)</span></span>
</div>
<p class="pardesc">Values of the generic parameters that can be adjusted
for the LUT classifier creation.</p>
<p class="pardesc"><span class="parcat">Default:
      </span>[]</p>
<p class="pardesc"><span class="parcat">Suggested values:
      </span>8, 7, 6, 0.5, 5, 10, 50</p>
</div>
  <div class="par">
<div class="parhead">
<span id="ClassLUTHandle" class="parname"><b><code><span data-if="hdevelop" style="display:inline">ClassLUTHandle</span><span data-if="c" style="display:none">ClassLUTHandle</span><span data-if="cpp" style="display:none">ClassLUTHandle</span><span data-if="com" style="display:none">ClassLUTHandle</span><span data-if="dotnet" style="display:none">classLUTHandle</span><span data-if="python" style="display:none">class_luthandle</span></code></b> (output_control)  </span><span>class_lut <code>→</code> <span data-if="dotnet" style="display:none"><a href="HClassLUT.html">HClassLUT</a>, </span><span data-if="dotnet" style="display:none"><a href="HTuple.html">HTuple</a></span><span data-if="python" style="display:none">HHandle</span><span data-if="cpp" style="display:none"><a href="HTuple.html">HTuple</a></span><span data-if="c" style="display:none">Htuple</span><span data-if="hdevelop" style="display:inline"> (handle)</span><span data-if="dotnet" style="display:none"> (<i>IntPtr</i>)</span><span data-if="cpp" style="display:none"> (<i>HHandle</i>)</span><span data-if="c" style="display:none"> (<i>handle</i>)</span></span>
</div>
<p class="pardesc">Handle of the LUT classifier.</p>
</div>
<h2 id="sec_result">结果</h2>
<p>如果参数均有效，算子 <code><span data-if="hdevelop" style="display:inline">create_class_lut_knn</span><span data-if="c" style="display:none">create_class_lut_knn</span><span data-if="cpp" style="display:none">CreateClassLutKnn</span><span data-if="com" style="display:none">CreateClassLutKnn</span><span data-if="dotnet" style="display:none">CreateClassLutKnn</span><span data-if="python" style="display:none">create_class_lut_knn</span></code>
返回值 <TT>2</TT> (
      <TT>H_MSG_TRUE</TT>)
    . If necessary an exception is raised.</p>
<h2 id="sec_predecessors">可能的前置算子</h2>
<p>
<code><a href="train_class_knn.html"><span data-if="hdevelop" style="display:inline">train_class_knn</span><span data-if="c" style="display:none">train_class_knn</span><span data-if="cpp" style="display:none">TrainClassKnn</span><span data-if="com" style="display:none">TrainClassKnn</span><span data-if="dotnet" style="display:none">TrainClassKnn</span><span data-if="python" style="display:none">train_class_knn</span></a></code>, 
<code><a href="read_class_knn.html"><span data-if="hdevelop" style="display:inline">read_class_knn</span><span data-if="c" style="display:none">read_class_knn</span><span data-if="cpp" style="display:none">ReadClassKnn</span><span data-if="com" style="display:none">ReadClassKnn</span><span data-if="dotnet" style="display:none">ReadClassKnn</span><span data-if="python" style="display:none">read_class_knn</span></a></code>
</p>
<h2 id="sec_successors">可能的后置算子</h2>
<p>
<code><a href="classify_image_class_lut.html"><span data-if="hdevelop" style="display:inline">classify_image_class_lut</span><span data-if="c" style="display:none">classify_image_class_lut</span><span data-if="cpp" style="display:none">ClassifyImageClassLut</span><span data-if="com" style="display:none">ClassifyImageClassLut</span><span data-if="dotnet" style="display:none">ClassifyImageClassLut</span><span data-if="python" style="display:none">classify_image_class_lut</span></a></code>
</p>
<h2 id="sec_alternatives">可替代算子</h2>
<p>
<code><a href="create_class_lut_svm.html"><span data-if="hdevelop" style="display:inline">create_class_lut_svm</span><span data-if="c" style="display:none">create_class_lut_svm</span><span data-if="cpp" style="display:none">CreateClassLutSvm</span><span data-if="com" style="display:none">CreateClassLutSvm</span><span data-if="dotnet" style="display:none">CreateClassLutSvm</span><span data-if="python" style="display:none">create_class_lut_svm</span></a></code>, 
<code><a href="create_class_lut_gmm.html"><span data-if="hdevelop" style="display:inline">create_class_lut_gmm</span><span data-if="c" style="display:none">create_class_lut_gmm</span><span data-if="cpp" style="display:none">CreateClassLutGmm</span><span data-if="com" style="display:none">CreateClassLutGmm</span><span data-if="dotnet" style="display:none">CreateClassLutGmm</span><span data-if="python" style="display:none">create_class_lut_gmm</span></a></code>, 
<code><a href="create_class_lut_mlp.html"><span data-if="hdevelop" style="display:inline">create_class_lut_mlp</span><span data-if="c" style="display:none">create_class_lut_mlp</span><span data-if="cpp" style="display:none">CreateClassLutMlp</span><span data-if="com" style="display:none">CreateClassLutMlp</span><span data-if="dotnet" style="display:none">CreateClassLutMlp</span><span data-if="python" style="display:none">create_class_lut_mlp</span></a></code>
</p>
<h2 id="sec_see">参考其它</h2>
<p>
<code><a href="classify_image_class_lut.html"><span data-if="hdevelop" style="display:inline">classify_image_class_lut</span><span data-if="c" style="display:none">classify_image_class_lut</span><span data-if="cpp" style="display:none">ClassifyImageClassLut</span><span data-if="com" style="display:none">ClassifyImageClassLut</span><span data-if="dotnet" style="display:none">ClassifyImageClassLut</span><span data-if="python" style="display:none">classify_image_class_lut</span></a></code>, 
<code><a href="clear_class_lut.html"><span data-if="hdevelop" style="display:inline">clear_class_lut</span><span data-if="c" style="display:none">clear_class_lut</span><span data-if="cpp" style="display:none">ClearClassLut</span><span data-if="com" style="display:none">ClearClassLut</span><span data-if="dotnet" style="display:none">ClearClassLut</span><span data-if="python" style="display:none">clear_class_lut</span></a></code>
</p>
<h2 id="sec_module">模块</h2>
<p>
Foundation</p>
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